Name
Abstract | ||
|---|---|---|
Social networks generate a massive amount of interaction data among users in the form of streams. To facilitate social network users to consume the continuously generated stream and identify preferred viral social contents, we present a real-time monitoring system called River to track a small set of influential social contents from high-speed streams in this demo. River has four novel features which distinguish itself from existing social monitoring systems: (1) River extracts a set of contents which collectively have the most significant influence coverage while reducing the influence overlaps; (2) River is topic-based and monitors the contents which are relevant to users' preferences; (3) River is location-aware, i.e., it enables user influence query on the contents falling into the region of interests; and (4) River employs a novel sparse influential checkpoint (SIC) index to support efficient updates against the streaming rates of real world social networks in real-time. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICDMW.2018.00203 | 2018 18TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW) |
Keywords | Field | DocType |
Social network analysis, influence maximization, Twitter, location-based service | Data mining,Social media,Social network,Monitoring system,Computer science,STREAMS,Database | Conference |
ISSN | Citations | PageRank |
2375-9232 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mo Sha | 1 | 251 | 23.52 |
| Yuchen Li | 2 | 204 | 24.19 |
| Yanhao Wang | 3 | 60 | 5.62 |
| Wentian Guo | 4 | 19 | 1.94 |
| Kian-Lee Tan | 5 | 6962 | 776.65 |